Selected Variables

Correction Approach Selected

base: Code of the patient
covariates:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- RSA
- RPV
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
outcomes_ql:
- 2Y. ODI - Score (%)
- 2Y. SRS22 - SRS Subtotal score
- 2Y. SF36 - MCS
- 2Y. SF36 - PCS
outcomes_radiology:
- 6W. Major curve Cobb angle
- 1Y. Major curve Cobb angle
- 6W. T1 Sagittal Tilt
- 1Y. T1 Sagittal Tilt
- 6W. Sagittal Balance
- 1Y. Sagittal Balance
- 6W. Global Tilt
- 1Y. Global Tilt
- 6W. Lordosis (top of L1-S1)
- 1Y. Lordosis (top of L1-S1)
- 6W. LGap
- 1Y. LGap
- 6W. Pelvic Tilt
- 1Y. Pelvic Tilt
- 6W. RSA
- 1Y. RSA
- 6W. RPV
- 1Y. RPV
- 6W. RLL
- 1Y. RLL
predictive:
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Osteotomy
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Tobacco use_First Visit
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
demographic:
- Age
- Gender
- Prior Spine Surgery
- ASA classification
- 3CO
- BMI_First Visit
- Global Tilt
- Ideal LL
- Lordosis (top of L1-S1)
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
expanded:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- RSA
- RPV
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
- SRS22 - SRS Subtotal score_First Visit
- T1 Sagittal Tilt
- Sagittal Balance
- Global Tilt
- Lordosis (top of L1-S1)
- Pelvic Tilt

Propensity Scores Common Support

Model Stats

  • Treatment proportion: 0.043
  • Model Type: elastic_net
  • Accuracy: 0.9615385
  • Params: alpha: 0.3307692 lambda: 0.0613104

Average Treatment Effects - Radiology

Outcome: 6W. Major curve Cobb angle
Distribution:
      0%      25%      50%      75%     100% 
-72.0000 -21.1525 -10.9750  -4.0000  27.5500 
Model Type Y: boosting 
RMSE: 19.8978698749769 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 14.0756200341305 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -5.078 (Std.Error: 9.914)
Trimmed ATE (Yes-No): -5.059 (Std.Error: 9.952)
Upper ATE (Yes-No): -10.837 (Std.Error: 7.64)
Observational differences in treatment 10.185 (Yes-No) 

   treatment  outcome
1:       Yes 31.08333
2:        No 20.89832
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. Major curve Cobb angle
Distribution:
     0%     25%     50%     75%    100% 
-64.000 -22.785 -10.000  -3.000  22.440 
Model Type Y: boosting 
RMSE: 25.3476420628841 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 14.8532849064558 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): 3.102 (Std.Error: 16.887)
Trimmed ATE (Yes-No): 3.156 (Std.Error: 16.951)
Upper ATE (Yes-No): -11.879 (Std.Error: 5.978)
Observational differences in treatment 12.32 (Yes-No) 

   treatment  outcome
1:       Yes 33.09727
2:        No 20.77746
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-22.181906  -6.000000  -1.505162   1.596953  18.000000 
Model Type Y: boosting 
RMSE: 6.55098642786476 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 6.14820212622916 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -3.276 (Std.Error: 2.206)
Trimmed ATE (Yes-No): -3.321 (Std.Error: 2.213)
Upper ATE (Yes-No): 9.191 (Std.Error: 3.845)
Observational differences in treatment -1.633 (Yes-No) 

   treatment   outcome
1:       Yes -4.428921
2:        No -2.795822
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-22.000000  -6.000000  -2.018531   1.038160  20.000000 
Model Type Y: boosting 
RMSE: 7.48667263379032 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 5.80326598123684 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -1.148 (Std.Error: 2.795)
Trimmed ATE (Yes-No): -1.16 (Std.Error: 2.805)
Upper ATE (Yes-No): 1.641 (Std.Error: 2.575)
Observational differences in treatment -1.468 (Yes-No) 

   treatment   outcome
1:       Yes -4.182568
2:        No -2.714373
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Sagittal Balance
Distribution:
       0%       25%       50%       75%      100% 
-192.0000  -69.0075  -30.0050   -0.7175   89.0000 
Model Type Y: boosting 
RMSE: 70.6558062414318 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875

Model Type No: boosting 
RMSE: 54.5761709305995 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -32.574 (Std.Error: 15.102)
Trimmed ATE (Yes-No): -32.856 (Std.Error: 15.14)
Upper ATE (Yes-No): 43.802 (Std.Error: 30.333)
Observational differences in treatment -25.344 (Yes-No) 

   treatment  outcome
1:       Yes  7.27750
2:        No 32.62181
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. Sagittal Balance
Distribution:
       0%       25%       50%       75%      100% 
-192.5100  -67.2100  -30.3250    6.0475   89.3700 
Model Type Y: boosting 
RMSE: 57.4184876558042 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 53.0734665696215 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -13.603 (Std.Error: 28.816)
Trimmed ATE (Yes-No): -13.545 (Std.Error: 28.92)
Upper ATE (Yes-No): -27.532 (Std.Error: 21.892)
Observational differences in treatment -21.857 (Yes-No) 

   treatment  outcome
1:       Yes 14.62818
2:        No 36.48515
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Global Tilt
Distribution:
     0%     25%     50%     75%    100% 
-57.000 -18.115  -6.090   1.880 149.410 
Model Type Y: boosting 
RMSE: 16.0779262895749 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 15.3615337841689 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -6.104 (Std.Error: 7.227)
Trimmed ATE (Yes-No): -6.111 (Std.Error: 7.247)
Upper ATE (Yes-No): -4.155 (Std.Error: 3.924)
Observational differences in treatment -10.538 (Yes-No) 

   treatment  outcome
1:       Yes 14.05667
2:        No 24.59476
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. Global Tilt
Distribution:
     0%     25%     50%     75%    100% 
-37.060 -16.205  -5.790   1.000  26.000 
Model Type Y: boosting 
RMSE: 16.0462291932669 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875

Model Type No: boosting 
RMSE: 11.9796029703238 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -1.161 (Std.Error: 7.484)
Trimmed ATE (Yes-No): -1.121 (Std.Error: 7.512)
Upper ATE (Yes-No): -11.204 (Std.Error: 5.982)
Observational differences in treatment -7.413 (Yes-No) 

   treatment  outcome
1:       Yes 18.17727
2:        No 25.59033
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Lordosis (top of L1-S1)
Distribution:
    0%    25%    50%    75%   100% 
-71.00 -24.00  -9.85   0.00  29.00 
Model Type Y: boosting 
RMSE: 25.2907330796914 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 16.4040810790006 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -11.812 (Std.Error: 13.863)
Trimmed ATE (Yes-No): -11.815 (Std.Error: 13.908)
Upper ATE (Yes-No): -10.968 (Std.Error: 9.168)
Observational differences in treatment 6.134 (Yes-No) 

   treatment   outcome
1:       Yes -43.85917
2:        No -49.99317
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. Lordosis (top of L1-S1)
Distribution:
    0%    25%    50%    75%   100% 
-67.87 -25.00  -8.01   0.00  23.38 
Model Type Y: boosting 
RMSE: 32.1424583795345 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 15.5040484722692 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -7.82 (Std.Error: 21.104)
Trimmed ATE (Yes-No): -7.743 (Std.Error: 21.171)
Upper ATE (Yes-No): -29.105 (Std.Error: 19.697)
Observational differences in treatment 6.933 (Yes-No) 

   treatment   outcome
1:       Yes -42.60727
2:        No -49.53981
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. LGap
Distribution:
      0%      25%      50%      75%     100% 
-71.0000 -24.5400  -9.3722   0.5004  78.9200 
Model Type Y: boosting 
RMSE: 21.9610866757647 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 17.7876870511449 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -4.621 (Std.Error: 14.567)
Trimmed ATE (Yes-No): -4.6 (Std.Error: 14.608)
Upper ATE (Yes-No): -11.388 (Std.Error: 11.41)
Observational differences in treatment -0.083 (Yes-No) 

   treatment  outcome
1:       Yes 13.26453
2:        No 13.34759
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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Outcome: 1Y. LGap
Distribution:
      0%      25%      50%      75%     100% 
-67.7242 -24.9922  -8.0484   0.2242  22.0800 
Model Type Y: boosting 
RMSE: 28.0694124034301 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 15.7271163570723 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -4.764 (Std.Error: 20.751)
Trimmed ATE (Yes-No): -4.68 (Std.Error: 20.828)
Upper ATE (Yes-No): -28.026 (Std.Error: 18.547)
Observational differences in treatment 1.279 (Yes-No) 

   treatment  outcome
1:       Yes 14.72695
2:        No 13.44834
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
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Outcome: 6W. Pelvic Tilt
Distribution:
      0%      25%      50%      75%     100% 
-31.0000  -8.2775  -2.0750   2.1575  14.4200 
Model Type Y: boosting 
RMSE: 12.7455506753956 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 7.89178362857626 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -2.599 (Std.Error: 6.433)
Trimmed ATE (Yes-No): -2.576 (Std.Error: 6.457)
Upper ATE (Yes-No): -9.844 (Std.Error: 4.419)
Observational differences in treatment -7.171 (Yes-No) 

   treatment  outcome
1:       Yes 14.74833
2:        No 21.91911
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Pelvic Tilt
Distribution:
     0%     25%     50%     75%    100% 
-26.620  -7.000  -2.005   2.000  23.000 
Model Type Y: boosting 
RMSE: 14.459374990526 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 6.81820791113544 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): 1.776 (Std.Error: 5.517)
Trimmed ATE (Yes-No): 1.822 (Std.Error: 5.534)
Upper ATE (Yes-No): -10.724 (Std.Error: 4.89)
Observational differences in treatment -4.85 (Yes-No) 

   treatment  outcome
1:       Yes 17.72000
2:        No 22.57034
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RSA
Distribution:
      0%      25%      50%      75%     100% 
-57.0000 -18.0867  -6.2346   2.1227  76.5028 
Model Type Y: boosting 
RMSE: 14.0698862799472 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 13.4871989267944 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -5.375 (Std.Error: 5.123)
Trimmed ATE (Yes-No): -5.383 (Std.Error: 5.14)
Upper ATE (Yes-No): -2.926 (Std.Error: 3.684)
Observational differences in treatment -5.06 (Yes-No) 

   treatment   outcome
1:       Yes  7.222267
2:        No 12.282560
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RSA
Distribution:
      0%      25%      50%      75%     100% 
-37.0000 -16.5150  -5.7076   1.0000  25.0400 
Model Type Y: boosting 
RMSE: 17.1253513682169 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 11.6424668760257 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): 1.348 (Std.Error: 7.387)
Trimmed ATE (Yes-No): 1.397 (Std.Error: 7.421)
Upper ATE (Yes-No): -11.036 (Std.Error: 7.289)
Observational differences in treatment -2.141 (Yes-No) 

   treatment  outcome
1:       Yes 11.15575
2:        No 13.29627
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RPV
Distribution:
        0%        25%        50%        75%       100% 
-85.555100  -2.242325   2.157300   8.204075  31.000000 
Model Type Y: boosting 
RMSE: 12.3777288499773 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 8.74292295844234 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): 3.548 (Std.Error: 6.391)
Trimmed ATE (Yes-No): 3.533 (Std.Error: 6.412)
Upper ATE (Yes-No): 8.507 (Std.Error: 4.736)
Observational differences in treatment 2.872 (Yes-No) 

   treatment   outcome
1:       Yes -5.097283
2:        No -7.969774
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RPV
Distribution:
      0%      25%      50%      75%     100% 
-22.1800  -1.4100   2.3588   6.4616  26.6346 
Model Type Y: boosting 
RMSE: 17.0631656170489 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 6.6686279195208 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -0.105 (Std.Error: 7.021)
Trimmed ATE (Yes-No): -0.139 (Std.Error: 7.046)
Upper ATE (Yes-No): 9.347 (Std.Error: 4.222)
Observational differences in treatment 0.435 (Yes-No) 

   treatment   outcome
1:       Yes -7.911764
2:        No -8.346553
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RLL
Distribution:
       0%       25%       50%       75%      100% 
-87.18180  -0.33685   9.36330  24.71500  71.00000 
Model Type Y: boosting 
RMSE: 20.109981812187 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 17.5892026454685 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): 8.158 (Std.Error: 15.355)
Trimmed ATE (Yes-No): 8.146 (Std.Error: 15.4)
Upper ATE (Yes-No): 12.147 (Std.Error: 10.17)
Observational differences in treatment 0.805 (Yes-No) 

   treatment   outcome
1:       Yes -13.34360
2:        No -14.14863
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RLL
Distribution:
       0%       25%       50%       75%      100% 
-22.58000  -0.37805   8.02520  25.03075  67.70260 
Model Type Y: boosting 
RMSE: 31.5515687669495 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 16.1077193874963 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): 2.392 (Std.Error: 21.139)
Trimmed ATE (Yes-No): 2.285 (Std.Error: 21.201)
Upper ATE (Yes-No): 31.743 (Std.Error: 16.937)
Observational differences in treatment -0.425 (Yes-No) 

   treatment   outcome
1:       Yes -14.83720
2:        No -14.41195
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'